April 10, 2026, Webb Hall 1100

Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which networked dynamical systems perform computation. In this talk, I extend this line of research to show that random heterogeneity in nodal parameters—often treated as disorder—can be exploited as a mechanism for stability and decision-making in neurocomputing systems. I first derive conditions under which the stability of desirable network-level states is promoted by disorder, revealing a direct link to the dimensionality of the underlying nodal dynamics. I then turn to decision-making systems, where I demonstrate that introducing disorder can significantly bias the system convergence toward (near-)global minimizers. These findings are supported through a combination of analytical results, numerical simulations, and experimental validation. The talk concludes by outlining future directions on energy-based dynamical models, with applications in associative memory, sparse optimization, and unconventional computing.

Arthur Montanari is a postdoctoral scholar at the Center for Network Dynamics at Northwestern University, USA. He received the M.Sc. and Ph.D. degrees in Electrical Engineering, with a focus on control theory and network dynamics, from the Federal University of Minas Gerais, Brazil, in 2018 and 2021, respectively. From 2021 to 2022, he was a postdoctoral fellow at the Luxembourg Centre for Systems Biomedicine, University of Luxembourg, where he specialized in the development of machine learning models for biomedical data. His current research focuses on network control, complex systems, and neurocomputation.